Active Management of Complex Non-self-finalising Behaviours through Deep Analytics

通过深度分析主动管理复杂的非自我完成行为

• 批准号: LP140100937
• 负责人: Prof Guandong Xu
• 金额: $ 25.38万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-08-05 至 2018-08-04
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP140100937
• 关键词: Active; Management; Complex; Non; self

This project aims to build theoretical breakthroughs and novel tools for deep analytics and active management of non-self-finalising (NSF) individual and business behaviours, which are sophisticated and increasingly seen in public sectors such as taxation and business including banking and insurance. The challenging economic environment continues to make managing NSF behaviours difficult. To date, there are no sufficient theories or effective systems in data mining and behavioural science to systematically learn the intent, impact and patterns of NSF behaviours, and to suggest cost-effective responses to these behaviours. This project aims to ensure Australia’s leading role in innovation for evidence-driven enterprise behaviour analytics and management.

该项目旨在建立理论的突破和新颖的工具，以深入分析和积极管理非自我基金（NSF）个人和商业行为，这些行为在税收和企业（包括银行业和保险）等公共部门中变得复杂且越来越多。挑战经济环境继续使管理NSF行为变得困难。迄今为止，在数据挖掘和行为科学中还没有足够的理论或有效的系统来系统地学习NSF行为的意图，影响和模式，并建议对这些行为的具有成本效益的响应。该项目旨在确保澳大利亚在循证企业行为分析和管理方面的创新中的领先作用。